There are several apps but it’s probably Microsoft Copilot which is readily available on any Windows computer. Here we asked it “Draw Apple Intelligence“. It didn’t quite get it…
Calling out Apple and others who keep this USB market going.
We only need one type of USB and all devices should be the same.
The European Union adopted a new USB legislation that makes USB-C the default standard.
Let’s do this in the United States, it will save time and money for everyone (except maybe Apple).
The name of SpaceX’s most recent mission sounds like something out of one of those sci-fi books you might find at your summer beach rental.
But it’s real. Multiple billionaires pushing the limits to send humans (including themselves) to the furthest we’ve ever gone – 850+ miles into space.
The launch was postponed due to a helium leak but you can follow along here.
https://x.com/SpaceX
I keep hearing this term ‘protect free speech’.
In today’s age this is nonsense.
We are so overpowered by what is being injected into our psyches from scrolling that it won’t matter if we have free speech or not. The machines are making us think what they and others want.
Unplug and remove social apps from your phone! Get a bird feeder and a book.
In recent years, language models have transformed the way we interact with technology, making significant strides in natural language understanding and generation. However, the impressive capabilities of these models come with a steep price—an immense consumption of energy. One staggering statistic often cited is that training a large language model can require energy equivalent to the output of a nuclear power plant. To understand why this is the case, let’s dive into the intricacies of token training in language models and the computational demands it imposes.
Language models, such as GPT (Generative Pre-trained Transformer) models, are trained using vast datasets composed of text from books, articles, websites, and more. These models process text as sequences of tokens—units that can be as small as individual characters or as large as whole words. Training involves adjusting the model’s parameters to predict the next token in a sequence accurately. This process is repeated billions of times across massive datasets to fine-tune the model’s understanding and generation capabilities.
The sheer volume of data used for training is staggering. For instance, GPT-3, one of the largest language models developed by OpenAI, was trained on hundreds of billions of tokens. Processing this amount of data requires not only substantial storage but also significant computational power. Each training iteration involves complex mathematical operations, including matrix multiplications and transformations, which must be performed across numerous layers of the neural network.
To handle such extensive computations, language model training relies on high-performance hardware, particularly Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs). These specialized processors are designed to accelerate the computations required for machine learning tasks. However, the energy consumption of these processors is considerable. A single GPU can consume hundreds of watts of power, and training a state-of-the-art language model often involves using thousands of GPUs simultaneously.
Training a large language model is not a quick task. It can take weeks or even months of continuous processing to achieve the desired level of performance. During this time, the GPUs or TPUs are running at full capacity, consuming power around the clock. The extended duration of training, combined with the high energy demands of the hardware, contributes significantly to the overall energy consumption.
In addition to the power consumed by the processors themselves, there are other energy costs associated with maintaining the infrastructure required for training. Data centers housing the hardware must be kept cool to prevent overheating, necessitating sophisticated cooling systems that consume additional power. The infrastructure also includes networking equipment, storage systems, and other auxiliary components that contribute to the total energy footprint.
The energy consumption of training large language models has a tangible environmental impact. The carbon footprint of such training runs is significant, contributing to greenhouse gas emissions unless offset by renewable energy sources. This has led to growing concerns and discussions within the AI community about the sustainability of developing ever-larger models.
Training large language models involves processing immense amounts of data through complex computations that require high-performance hardware running continuously for extended periods. This results in substantial energy consumption, comparable to the output of a nuclear power plant. As the capabilities of language models continue to advance, it becomes increasingly crucial to explore more efficient training methods and sustainable energy solutions to mitigate the environmental impact. The future of AI will not only depend on technological advancements but also on our ability to balance progress with responsible energy use.
I used to think no company could reach $32 Trillion in Market Cap. Companies like Apple have just passed $1 Trillion.
After I listened to Elon Musk speak recently, I am not so sure this is out of question. $32 Trillion sounds insane. How can a car company ever be valued like that you ask?
Firstly, the cars will only be a fraction of their revenue in the near future. Tesla is also a leading battery company and now with the new Optimus robot, he’s making a case it could be very very profitable in the future. This doesn’t include how they will monetize autonomy.
With more than a 1,000 Optimus robots scheduled to start working in Tesla factories this year, Musk believes Tesla is well positioned to own the humanoid robotics market. His calculations assume at least 1 robot for every person on earth.
At a $10K cost to build and a sales price of $20k, the Optimus robots could actually be a huge cash cow for the company.
The question in my opinion: do we all actually need and want a personal robot for $20k.
I am not sure I do…? Plus the human form freaks me out due to the Uncanny Valley.
Watch Elon speak at the 2024 Tesla ($TSLA) Shareholder meeting below.
Currently organizations involved in the A.I. race are also buying or building nuclear power plants. Why? AI models, especially large-scale deep learning models, require significant computational resources, which in turn demand substantial amounts of electricity for several reasons:
Efforts are underway to optimize AI algorithms, develop more energy-efficient hardware, and implement sustainable practices in data center operations to mitigate the environmental impact of AI’s electricity consumption. However, the inherent computational demands of AI tasks mean that electricity consumption remains a significant consideration in deploying and scaling AI technologies.
In the context of training AI models, a token generally refers to a single unit of input data that the model processes at one time. The term “token” can be used in various ways depending on the specific type of AI model and its architecture, but here are a few common interpretations:
The size and nature of tokens can vary greatly depending on the specific AI model architecture and the requirements of the task. Tokens are fundamental to how models perceive and process input data, making them a critical concept in AI training and inference.
I was fortunate to have spent quite a few years working with data and robotics. I didn’t really choose the field, it chose me. One of the highlights of my career was going through a McDonald’s drive thru with 3 of our BiBlI robots strapped in the backseat. When I got to the window I asked them if they wanted anything which caused the McDonald’s staff to laugh.
I didn’t realize at the time that I was learning a valuable skill – how to talk to robots.
Not for real as in how to take their burger order – although voice is a facinating aspect of machine learning and robotics – but more how to interface.
Robots need data and they still need our help to set context and guide them. We’ve seen that recently with the sudden explosion of ChatGPT powered A.I. startups and ‘experts’ who simply have interfaced well with the underlying Artificial Intelligence.
A.I. headlines this morning (January 24, 2024) talk about tech companies laying off staff. This is just the beginning! If your role is only pushing buttons, pulling levers, compiling data – or even writing code or blog posts like this – you need to upskill quickly.
From January 2024 to January 2025 the acceleration of A.I. in our society will explode. Most of it will be good, but newfound efficiencies in business and production of any kind are coming. Get ready!
That’s a mouthful and what does it have to do with Artificial Intelligence
Search Engine Optimization, generally known as “SEO” is an age-old tactic of trying to manipulate the Search Engines so that your website shows up before another when someone searches. Sounds boring, right?
It’s actually likely one of the most urgent any CEO faces today. What hidden signals and meta data is your organization sending?
In other words, how does your website and digital footprint index in the world of machine-driven algorithms?
Technologies like Siri and Google read these signals and index this data to re-present your brand and website to the world. They generally need clean, clear info and a good user experience.
Not sure where to start? Try checking how Google views your current website. Site usability and speed is a key signal you are sending that tells their computers how to rank and index you.
Additional Resource: Properly Tag Your Website with Google Analytics 4 (GA4) to Send the Right Signals
